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Hands-On Deep Learning for Games

You're reading from   Hands-On Deep Learning for Games Leverage the power of neural networks and reinforcement learning to build intelligent games

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Product type Paperback
Published in Mar 2019
Publisher Packt
ISBN-13 9781788994071
Length 392 pages
Edition 1st Edition
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Author (1):
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Micheal Lanham Micheal Lanham
Author Profile Icon Micheal Lanham
Micheal Lanham
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Toc

Table of Contents (18) Chapters Close

Preface 1. Section 1: The Basics FREE CHAPTER
2. Deep Learning for Games 3. Convolutional and Recurrent Networks 4. GAN for Games 5. Building a Deep Learning Gaming Chatbot 6. Section 2: Deep Reinforcement Learning
7. Introducing DRL 8. Unity ML-Agents 9. Agent and the Environment 10. Understanding PPO 11. Rewards and Reinforcement Learning 12. Imitation and Transfer Learning 13. Building Multi-Agent Environments 14. Section 3: Building Games
15. Debugging/Testing a Game with DRL 16. Obstacle Tower Challenge and Beyond 17. Other Books You May Enjoy

Exercises

Attempt one or two of the following exercises on your own:

  1. Run the CrawlerStaticTarget example scene and compare its performance to the dynamic sample.
  1. Double the time_horizon, batch_size, and buffer_size brain hyperparameters in one of the other control examples:
time_horizon: 2000
batch_size: 4048
buffer_size: 40480
  1. Perform the same modification of time_horizon, batch_size, and buffer_size on another control sample and observe the combined effect.
  2. Modify the num_layers and hidden_units brain hyperparameters to values we used in a control sample and apply them to a discrete action example, such as the Hallway example, as shown in the following code. How did it affect training?
num_layers: 3
hidden_units: 512
  1. Alter the num_layers and hidden_units hyperparameters on another continuous or discrete action example and combine it with other parameter modifications.
  2. Modify...
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